The present invention pertains to probabilistic troubleshooters and diagnostic systems and pertains particularly to model selection for decision support systems.
Decision support systems are defined as capturing systems for diagnosis, troubleshooting, selection, classification, prediction and general decision support.
Currently, it is highly expensive for manufacturers to diagnose the systems of their customers. Automation of this process has been attempted using probabilistic troubleshooters and other diagnostic systems. Some of these systems are based on Bayesian networks.
One troubleshooter based on Bayesian networks is described by Heckerman, D., Breese, J., and Rommelse, K. (1995), Decision-theoretic Troubleshooting, Communications of the ACM, 38:49-57 (herein “Heckerman et al. 1995”).
In scientific literature Bayesian networks are referred to by various names: Bayes nets, causal probabilistic networks, Bayesian belief networks or simply belief networks. Loosely defined Bayesian networks are a concise (acyclic) graphical structure for modeling probabilistic relationships among discrete random variables. Bayesian networks are used to efficiently model problem domains containing uncertainty in some manner and therein lies their utility. Since they can be easily modeled on a computer, they are the subject of increasing interest and use in automated decision-support systems, whether for medical diagnosis, automated automotive troubleshooting, economic or stock market forecasting or in other areas as mundane as predicting a computer user's likely requirements.
In general, a Bayesian network consists of a set of nodes representing discrete-valued variables connected by arcs representing the causal dependencies between the nodes. A set of conditional probability tables, one for each node, defines the dependency between the nodes and its parents. And, nodes without parents, sometimes called source nodes, have associated therewith a prior marginal probability table. For specific applications the data for the probability tables for all other nodes are provided by what is termed domain experts in whatever field is being modeled. This involves assigning prior probabilities for all nodes without parents, and conditional probabilities for all nodes with parents. In diagnostic Bayesian networks nodes can represent causes, or outcomes of actions and questions. In very large diagnostic Bayesian networks, most of the events are very rare with probabilities in the range of 0.001 to 0.000001. But, since a primary goal of a computer decision support system is to provide decisions as accurate as is possible, it is imperative that the domain experts provide probabilistic information that is highly reliable and their best estimate of the situation.
Bayesian networks provide a way to model problem areas using probability theory. The Bayesian network representation of a problem can be used to provide information on a subset of variables given information on others. A Bayesian network consists of a set of variables (nodes) and a set of directed edges (connections between variables). Each variable has a set of mutually exclusive states. The variables together with the directed edges form a directed acyclic graph (DAG). For each variable υ with parents w1, . . . , wn, there is defined a conditional probability table P(υ|w1, . . . , wn). Obviously, if v has no parents, this table reduces to the marginal probability P(υ).
Bayesian networks have been used in many application domains with uncertainty, such as medical diagnosis, pedigree analysis, planning, debt detection, bottleneck detection, etc. However, one of the major application areas has been diagnosis. Diagnosis (i.e., underlying factors that cause diseases/malfunctions that again cause symptoms) lends itself nicely to the modeling techniques of Bayesian networks.
Model selection is the ability to aid a user of a diagnostic system in determining the correct model for handling a problem or helping the user reach a decision.
Menu based selection of models can incorporate a tree of models in menus and submenus. This provides a user with an overview of the available models, however, it can be difficult to find the correct model in a large tree of models. Also, it may not be possible for an inexperienced user to identify the correct model. For example, “Bubble print” is a clearly defined print quality problem on printers; however, only expert users will be able to classify an obscure print quality problem as “Bubble print”.
Text search selection of models operate by using text search within sub models to determine which sub model to use. Text searching occasionally allows short cutting directly to the desired model, however, if the description of the problem is unknown to the user (e.g., “Bubble print”), the user will be unable to supply a good text to find the best model.
Case based systems can be used for model selection as such case based systems are intended to help users identify problems by asking a sequence of questions. Case based systems for model selection do, however, suffer from the same problems as all other case based systems. Constructing a case base system requires a detailed technical knowledge of cased based systems as the performance of the system is very dependent on the quality of cases used for inference.